Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution Under Progressive Type-II Censoring

Muhammed, Hiba Z.; Almetwally, Ehab M.

doi:10.1007/s40745-020-00316-7

Cited by 32 publications

(26 citation statements)

References 18 publications

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“…We suggest studying a more flexible censored sample for the proposed distribution in future studies, such as [38,39]. We suggest researching multivariate analysis, such as [40,41] for the proposed distribution.…”

Section: Discussionmentioning

confidence: 99%

Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring

Almetwally

2022

Gazi University Journal of Science

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Section: Discussionmentioning

confidence: 99%

Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring

Almetwally

2022

Gazi University Journal of Science

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“…For more details regarding the Metropolis-Hasting algorithm's implementation, we refer the readers to the works of Hafez et al [25], Nassar et al [33], Muhammed and. Almetwally [34], and Almetwally et al [35].…”

Section: Bayesian Estimationmentioning

confidence: 97%

Marshall–Olkin Alpha Power Weibull Distribution: Different Methods of Estimation Based on Type‐I and Type‐II Censoring

et al. 2021

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This paper introduces the new novel four-parameter Weibull distribution named as the Marshall–Olkin alpha power Weibull (MOAPW) distribution. Some statistical properties of the distribution are examined. Based on Type-I censored and Type-II censored samples, maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation for the MOAPW distribution parameters are discussed. Numerical analysis using real data sets and Monte Carlo simulation are accomplished to compare various estimation methods. This novel model’s supremacy upon some famous distributions is explained using two real data sets and it is shown that the MOAPW model can achieve better fits than other competitive distributions.

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“…Many authors discussed the inverted distributions and their applications. Some of the well-known inverted models are inverse Weibull distribution (Calabria and Pulcini [7] , Muhammed and Almetwally [8] , [9] ), inverted Topp-Leone (ITL) (Hassan et al. [10] , Almetwally et al.…”

Section: Introductionmentioning

confidence: 99%

The new discrete distribution with application to COVID-19 Data

et al. 2022

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This research aims to model the COVID-19 in different countries, including Italy, Puerto Rico, and Singapore. Due to the great applicability of the discrete distributions in analyzing count data, we model a new novel discrete distribution by using the survival discretization method. Because of importance Marshall- Olkin family and the inverse Toppe-Leone distribution, both of them were used to introduce a new discrete distribution called Marshall–Olkin inverse Toppe-Leone distribution, this new distribution namely the new discrete distribution called discrete Marshall- Olkin Inverse Toppe-Leone (DMOITL). This new model posses only two parameters, also many properties have been obtained such as reliability measures and moment functions. The classical method as likelihood method and Bayesian estimation methods are applied to estimate the unknown parameters of DMOITL distributions. The Monte–Carlo simulation procedure is carried out to compare the maximum likelihood and Bayesian estimation methods. The highest posterior density (HPD) confidence intervals are used to discuss credible confidence intervals of parameters of new discrete distribution for the results of the Markov Chain Monte Carlo technique (MCMC).

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Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution Under Progressive Type-II Censoring

Cited by 32 publications

References 18 publications

Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring

Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring

Marshall–Olkin Alpha Power Weibull Distribution: Different Methods of Estimation Based on Type‐I and Type‐II Censoring

The new discrete distribution with application to COVID-19 Data

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